COMPUTER MODEL OF VIRTUAL SOCIAL NETWORK WITH RECOMMENDATION SYSTEM
DOI:
https://doi.org/10.30837/2522-9818.2019.8.080Keywords:
social network, recommendation system, computer modeling, stochastic graphAbstract
The subject matter of the article is the process of modeling social networks. The goal is to develop a computer model of a social network with a recommendation system. The tasks to be solved are to research the methods of generating social networks, to realize the computer model of a social network with a recommender system. The methods used are graph theory, theory of algorithms, statistics theory, probability theory, object-oriented programming. The following results: the research of existing methods for modeling social networks was conducted, in particular, such social network models as the Barabasi-Albert model, the Erdős-Renyi model and the Bollobаs-Riordan model were considered. The concept of complex networks was considered. The research of the basic properties of graphs of social networks was considered. The social network computer model with a recommender system based on the modified Barabasi-Albert model with using graph database Neo4j and programming language Python was developed. The developed model allows to model a network with users and text posts and may contain following connections "friends", "follower", "published", "viewed", "like", "similar", "recommended", and also allows testing of algorithms of recommender systems and conduct research to changes in a social network after creating and proposing recommendations. The testing of the developed computer model of virtual social network with a recommender system was conducted. Conclusions. The research of various methods of modeling social networks was conducted. The concept of complex networks was investigated. The main properties of social network graphs are considered. The computer model of a social network with a recommendation system that contains various types of nodes and connections that allow testing a recommender system algorithm has been developed. The developed model of a social network with a recommender system was tested to check its similarity with real social networks. The developed computer model of a social network has the values of network graph parameters corresponding to the values characteristic of real social networks, which allows using the developed model to research the processes that can occur in real social networks.
References
Editors Ricci, F., Rokach, L., Shapira, B., Kantor, P. B. (2010), Recommender Systems Handbook, 1st edition, New York, NY, USA: Springer-Verlag New York, Inc., 842 p. DOI: https://doi.org/10.1007/978-0-387-85820-3
Segaran, T. (2008), Programming Collective Intelligence. Building Smart Web 2.0 Applications, O'Reilly Media, 368 p.
Meleshko, Ye. (2018), "Quality assessment methods of work of recommendation systems", Academic Journal "Control, Navigation and Communication Systems", Poltava National Technical Yuri Kondratyuk University, No. 5 (51), P. 92–97, DOI: https://doi.org/10.26906/SUNZ.2018.5.092 (in Ukrainian)
Gusarova, N. (2016), Social network analysis. Basic concepts and metrics, St. Petersburg : ITMO University, 67 p. (in Russian)
Batura, T. (2013), "Models and methods of analysis of computer social networking", Software products and systems, No. 3, available at : https://cyberleninka.ru/article/n/modeli-i-metody-analiza-kompyuternyh-sotsialnyh-setey (in Russian)
Churakov, A. (2001), "Social Network Analysis", Sociological studies, No. 1, P. 109–121. (in Russian)
Kochkarov, A., Sennikova, L., Kochkarov, R. (2015), "Some features of using dynamic graphs for constructing mobile subscriber interaction algorithms", Proceedings of Southern Federal University, Technical Sciences, No. 1 (162), available at : https://cyberleninka.ru/article/n/nekotorye-osobennosti-primeneniya-dinamicheskih-grafov-dlya-konstruirovaniya-algoritmov-vzaimodeystviya-podvizhnyh-abonentov (in Russian)
Gubanov, D., Novikov, D., Chhartishvili, A. (2010), Social networks: models of informational influence, control and confrontation – Second edition, Moscow, Physics and Mathematics Literature Publisher MCCME, 228 p. (in Russian)
Haidai, B., Artiukh, R., Malyeyeva, O. (2018), "Analysis and modelling the preferences of social networks users", Innovative technologies and scientific solutions for industries, No. 1 (3), P. 5–12. DOI: https://doi.org/10.30837/2522-9818.2018.3.005
Evin, I. (2010), "Introduction to the theory of complex networks", Computer research and modeling, Vol. 2, No. 2, P. 121–141 (in Russian)
Melikov, S., Musatov, D., Savvateev, A. (2013), "Modeling social networks", available at : https://kpfu.ru/docs/F117464271/MMS_socnet_cities.pdf (in Russian)
Albert, R., Barabasi, A.-L. (2002), "Statistical mechanics of complex networks", Rev. Mod. Phys., No. 74, P. 47–97, DOI: https://doi.org/10.1103/RevModPhys.74.47
Bernovskij, M., Kuzjurin, N. (2012), "Random graphs, models and generators of scale-free graphs", Proceedings of the Institute for System Programming of the Russian Academy of Sciences, Vol. 22, P. 419–432. (in Russian)
Rajgorodskij, A. (2012), "Mathematical models of the Internet", Kvant, No. 4, P. 12–16, available at : https://elementy.ru/nauchno-populyarnaya_biblioteka/431792 (in Russian)
"Wiki for project Gephi on GitHub" (2019), available at : https://github.com/gephi/gephi/wiki.
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